Localization for Multirobot Formations in Indoor Environment
ABSTRACT Localization is a key issue in multirobot formations, but it has not yet been sufficiently studied. In this paper, we propose a ceiling vision-based simultaneous localization and mapping (SLAM) methodology for solving the global localization problems in multirobot formations. First, an efficient data-association method is developed to achieve an optimistic feature match hypothesis quickly and accurately. Then, the relative poses among the robots are calculated utilizing a match-based approach, for local localization. To achieve the goal of global localization, three strategies are proposed. The first strategy is to globally localize one robot only (i.e., leader) and then localize the others based on relative poses among the robots. The second strategy is that each robot globally localizes itself by implementing SLAM individually. The third strategy is to utilize a common SLAM server, which may be installed on one of the robots, to globally localize all the robots simultaneously, based on a shared global map. Experiments are finally performed on a group of mobile robots to demonstrate the effectiveness of the proposed approaches.
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ABSTRACT: This paper proposes a distributed control approach called local interactions with local coordinate systems (LILCS)to multirobot hunting tasks in unknown environments, where a team of mobile robots hunts a target called evader, which will actively try to escape with a safety strategy. This robust approach can cope with accumulative errors of wheels and imperfect communication networks. Computer simulations show the validity of the proposed approach.IEEE Transactions on Robotics 05/2006; · 2.57 Impact Factor
Article: Multi-robot coalition formation[show abstract] [hide abstract]
ABSTRACT: As the community strives towards autonomous multi-robot systems, there is a need for these systems to autonomously form coalitions to complete assigned missions. Numerous coalition formation algorithms have been proposed in the software agent literature. Algorithms exist that form agent coalitions in both super additive and non-super additive environments. The algorithmic techniques vary from negotiation-based protocols in multi-agent system (MAS) environments to those based on computation in distributed problem solving (DPS) environments. Coalition formation behaviors have also been discussed in relation to game theory. Despite the plethora of MAS coalition formation literature, to the best of our knowledge none of the proposed algorithms have been demonstrated with an actual multi-robot system. There exists a discrepancy between the multi-agent algorithms and their applicability to the multi-robot domain. This paper aims to bridge that discrepancy by unearthing the issues that arise while attempting to tailor these algorithms to the multi-robot domain. A well-known multi-agent coalition formation algorithm has been studied in order to identify the necessary modifications to facilitate its application to the multi-robot domain. This paper reports multi-robot coalition formation results based upon simulation and actual robot experiments. A multi-agent coalition formation algorithm has been demonstrated on an actual robot systemIEEE Transactions on Robotics 09/2006; · 2.57 Impact Factor
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ABSTRACT: The cluster space state representation of mobile multirobot systems is introduced as a means of enabling enhanced control of mobile multirobot systems. A conceptual framework is proposed for the selection of appropriate cluster space state variables for an n -robot system, the development of formal kinematics that associate the cluster space state variables with robot-specific variables, and the implementation of a cluster space control system architecture. The cluster space approach is then demonstrated for examples of two- and three-robot clusters consisting of differential drive robots operating in a plane. In these examples, we demonstrate cluster space variable selection, review the critical kinematic relationships, and present experimental results that demonstrate the ability of the systems to meet control specifications while allowing a single operator to easily specify and supervise the motion of the clusters.IEEE/ASME Transactions on Mechatronics 05/2009; · 3.14 Impact Factor